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Antibodies represent a specific class of proteins produced by the adaptive immune system in response to pathogens. Mining the information embedded in antibody amino acid sequences can benefit both antibody property prediction and novel therapeutic development. However, antibodies possess unique features that should be incorporated using specifically designed training methods, leaving room for improvement in pre-training models for antibody sequences. In this study, we present a Pre-trained model of Antibody sequences trained with a Rational Approach for antibodies (PARA). PARA employs a strategy conforming to antibody sequence patterns and an advanced natural language processing self-encoding model structure. This approach addresses the limitations of existing protein pre-training models, which primarily utilize language models without fully considering the differences between protein sequences and language sequences. We demonstrate PARA's performance on several tasks by comparing it to various published pre-training models of antibodies. The results show that PARA significantly outperforms existing models on these tasks, suggesting that PARA has an advantage in capturing antibody sequence information. The antibody latent representation provided by PARA can substantially facilitate studies in relevant areas. We believe that PARA's superior performance in capturing antibody sequence information offers significant potential for both antibody property prediction and the development of novel therapeutics. PARA is available at https://github.com/xtalpi-xic.

Intellectual and Developmental Disabilities (IDDs), such as Down syndrome, Fragile X syndrome, Rett syndrome, and autism spectrum disorder, usually manifest at birth or early childhood. IDDs are characterized by significant impairment in intellectual and adaptive functioning, and both genetic and environmental factors underpin IDD biology. Molecular and genetic stratification of IDDs remain challenging mainly due to overlapping factors and comorbidity. Advances in high throughput sequencing, imaging, and tools to record behavioral data at scale have greatly enhanced our understanding of the molecular, cellular, structural, and environmental basis of some IDDs. Fueled by the “big data” revolution, artificial intelligence (AI) and machine learning (ML) technologies have brought a whole new paradigm shift in computational biology. Evidently, the ML-driven approach to clinical diagnoses has the potential to augment classical methods that use symptoms and external observations, hoping to push the personalized treatment plan forward. Therefore, integrative analyses and applications of ML technology have a direct bearing on discoveries in IDDs. The application of ML to IDDs can potentially improve screening and early diagnosis, advance our understanding of the complexity of comorbidity, and accelerate the identification of biomarkers for clinical research and drug development. For more than five decades, the IDDRC network has supported a nexus of investigators at centers across the USA, all striving to understand the interplay between various factors underlying IDDs. In this review, we introduced fast-increasing multi-modal data types, highlighted example studies that employed ML technologies to illuminate factors and biological mechanisms underlying IDDs, as well as recent advances in ML technologies and their applications to IDDs and other neurological diseases. We discussed various molecular, clinical, and environmental data collection modes, including genetic, imaging, phenotypical, and behavioral data types, along with multiple repositories that store and share such data. Furthermore, we outlined some fundamental concepts of machine learning algorithms and presented our opinion on specific gaps that will need to be filled to accomplish, for example, reliable implementation of ML-based diagnosis technology in IDD clinics. We anticipate that this review will guide researchers to formulate AI and ML-based approaches to investigate IDDs and related conditions.

Background Genotypes are strongly associated with disease phenotypes, particularly in brain disorders. However, the molecular and cellular mechanisms behind this association remain elusive. With emerging multimodal data for these mechanisms, machine learning methods can be applied for phenotype prediction at different scales, but due to the black-box nature of machine learning, integrating these modalities and interpreting biological mechanisms can be challenging. Additionally, the partial availability of these multimodal data presents a challenge in developing these predictive models. Method To address these challenges, we developed DeepGAMI, an interpretable neural network model to improve genotype–phenotype prediction from multimodal data. DeepGAMI leverages functional genomic information, such as eQTLs and gene regulation, to guide neural network connections. Additionally, it includes an auxiliary learning layer for cross-modal imputation allowing the imputation of latent features of missing modalities and thus predicting phenotypes from a single modality. Finally, DeepGAMI uses integrated gradient to prioritize multimodal features for various phenotypes. Results We applied DeepGAMI to several multimodal datasets including genotype and bulk and cell-type gene expression data in brain diseases, and gene expression and electrophysiology data of mouse neuronal cells. Using cross-validation and independent validation, DeepGAMI outperformed existing methods for classifying disease types, and cellular and clinical phenotypes, even using single modalities (e.g., AUC score of 0.79 for Schizophrenia and 0.73 for cognitive impairment in Alzheimer’s disease). Conclusion We demonstrated that DeepGAMI improves phenotype prediction and prioritizes phenotypic features and networks in multiple multimodal datasets in complex brains and brain diseases. Also, it prioritized disease-associated variants, genes, and regulatory networks linked to different phenotypes, providing novel insights into the interpretation of gene regulatory mechanisms. DeepGAMI is open-source and available for general use.

Reducing endogenous gene expression is key in microbial metabolic engineering. Traditional methods for gene knockout or suppression can be slow and complex. RNA interference (RNAi) provides a faster way to regulate gene expression using plasmids with hairpin RNA. This study examines single- and double-gene suppression in , a common system for expressing heterologous proteins. We also use reporter strains displaying EGFP on the cell surface to identify factors affecting protein secretion. We established an RNAi system in by introducing plasmids containing hairpin RNA targeting specific genes. Reporter strains expressing on the cell surface were used to monitor the impact of gene suppression on protein secretion. Genes such as , , , and were targeted for RNAi. Additionally, RNAi was applied to inhibit fatty acid synthesis to improve the conversion of malonyl-CoA to 3-hydroxypropionate (3-HP). Suppressing and reduced display by 83% and 48.8%, respectively. In contrast, suppressing and increased display by 33.8% and 26.5%, respectively. These findings show that regulating endogenous genes can significantly impact protein secretion in . Furthermore, RNAi inhibition of fatty acid synthesis improved 3-HP production. This study demonstrates the successful establishment of an RNAi system in , enabling efficient gene suppression for metabolic engineering. RNAi offers a faster and more efficient method for regulating gene expression, improving heterologous protein secretion and 3-HP production. This system is a valuable tool for optimizing as a microbial cell factory, with strong potential for industrial applications.

Several chemotherapeutic drugs induce senescence in cancer cells; however, the mechanisms underlying intracellular pH dysregulation in senescent cells remain unclear. Adenosine triphosphatase H + transporting accessory protein 2 (ATP6AP2) plays a critical role in maintaining pH homeostasis in cellular compartments. Herein, we report the regulatory role of ATP6AP2 in senescent breast cancer cells treated with doxorubicin (Doxo) and abemaciclib (Abe). A decline in ATP6AP2 triggers aberrant pH levels that impair lysosomal function and cause immune profile changes in senescent breast cancer cells. Doxo and Abe elicited a stable senescent phenotype and altered the expression of senescence-related genes. Additionally, senescent cells show altered inflammatory and immune transcriptional profiles due to reprogramming of the senescence-associated secretory phenotype. These findings elucidate ATP6AP2-mediated cellular pH regulation and suggest a potential link in immune profile alteration during therapy-induced senescence in breast cancer cells, providing insights into the mechanisms involved in the senescence response to anticancer therapy. Adenosine triphosphatase H + transporting accessory protein 2 (ATP6AP2) attenuation triggers intracellular acidification and lysosomal pH alkalinized in in senescent breast cancer cells induced by doxorubicin and abemaciclib, resulting in immune profile alterations through the reprogramming of senescence-associated secretory phenotype.

Unique molecular identifiers (MIDs) have been demonstrated to effectively improve immune repertoire sequencing (IR-seq) accuracy, especially to identify somatic hypermutations in antibody repertoire sequencing. However, evaluating the sensitivity to detect rare T cells and the degree of clonal expansion in IR-seq has been difficult due to the lack of knowledge of T cell receptor (TCR) RNA molecule copy number and a generalized approach to estimate T cell clone size from TCR RNA molecule quantification. This limited the application of TCR repertoire sequencing (TCR-seq) in clinical settings, such as detecting minimal residual disease in lymphoid malignancies after treatment, evaluating effectiveness of vaccination and assessing degree of infection. Here, we describe using an MID Clustering-based IR-Seq (MIDCIRS) method to quantitatively study TCR RNA molecule copy number and clonality in T cells. First, we demonstrated the necessity of performing MID sub-clustering to eliminate erroneous sequences. Further, we showed that MIDCIRS enables a sensitive detection of a single cell in as many as one million naïve T cells and an accurate estimation of the degree of T cell clonal expression. The demonstrated accuracy, sensitivity, and wide dynamic range of MIDCIRS TCR-seq provide foundations for future applications in both basic research and clinical settings.

Accurately measuring antibody repertoire sequence composition in a small amount of blood is challenging yet important for understanding repertoire responses to infection and vaccination. We develop molecular identifier clustering-based immune repertoire sequencing (MIDCIRS) and use it to study age-related antibody repertoire development and diversification before and during acute malaria in infants (< 12 months old) and toddlers (12–47 months old) with 4−8 ml of blood. Here, we show this accurate and high-coverage repertoire-sequencing method can use as few as 1000 naive B cells. Unexpectedly, we discover high levels of somatic hypermutation in infants as young as 3 months old. Antibody clonal lineage analysis reveals that somatic hypermutation levels are increased in both infants and toddlers upon infection, and memory B cells isolated from individuals who previously experienced malaria continue to induce somatic hypermutations upon malaria rechallenge. These results highlight the potential of antibody repertoire diversification in infants and toddlers. Somatic hypermutation of antibodies can occur in infants but are difficult to track. Here the authors present a new method called MIDCIRS for deep quantitative repertoire sequencing with few cells, and show infants as young as 3 months can expand antibody lineage complexity in response to malaria infection.

Cancers induce the production of acute phase proteins such as serum amyloid alpha (SAA) in the liver and cause inflammation in various host organs. Despite the well-known coincidence of acute phase response and inflammation, the direct roles of SAA proteins in inflammation in the cancer context remains incompletely characterized, particularly in vivo . Here, we investigate the in vivo significance of SAA proteins in liver inflammation in the 4T1 murine breast cancer model. 4T1 cancers elevate the expression of SAA1 and SAA2, the two major murine acute phase proteins in the liver. The elevation of Saa1-2 correlates with the up-regulation of immune cell-related genes including neutrophil markers. To examine this correlation in detail, we generate mice that lack Saa1-2 and investigate immune-cell phenotypes. RNA-seq experiments reveal that deletion of Saa1-2 does not strongly affect 4T1-induced activation of immune cell-related genes in the liver. Flow cytometry experiments demonstrate the dispensable roles of SAA1-2 in cancer-dependent neutrophil infiltration to the liver. Consistently, 4T1-induced gene expression changes in bone marrow do not require Saa1-2 . This study clarifies the negligible contribution of SAA1-2 proteins in liver inflammation in the 4T1 breast cancer model.

Follicular-helper T cells (T FH ) are an essential arm of the adaptive immune system. Although T FH were first discovered through their ability to contribute to antibody affinity maturation through co-stimulatory interactions with B cells, new light has been shed on their ability to remain a complex and functionally plastic cell type. Due to a lack sample availability, however, many studies have been limited to characterizing T FH in mice or non-canonical tissue types, such as peripheral blood. Such constraints have resulted in a limited, and sometimes contradictory, understanding of this fundamental cell type. One subset of T FH receiving attention in chronic infection are CXCR3-expressing T FH cells (CXCR3 + T FH ) due to their abnormal accumulation in secondary lymphoid tissues. Their function and clonal relationship with other T FH subsets in lymphoid tissues during infection, however, remains largely unclear. We thus systematically investigated this and other subsets of T FH within untreated HIV-infected human lymph nodes using Mass CyTOF and a combination of RNA and TCR repertoire sequencing. We show an inflation of the CXCR3 + T FH compartment during HIV infection that correlates with a lower HIV burden. Deeper analysis into this population revealed a functional shift of CXCR3 + T FH away from germinal center T FH (GC-T FH ), including the altered expression of several important transcription factors and cytokines. CXCR3 + T FH also upregulated cell migration transcriptional programs and were clonally related to peripheral T FH populations. In combination, these data suggest that CXCR3 + T FH have a greater tendency to enter circulation than their CXCR3 - counterparts, potentially functioning through distinct modalities that may lead to enhanced defense.

Regulatory T cells (Tregs) are essential for the maintenance of gut homeostasis by suppressing conventional CD4 helper T cells (Tconvs) that are activated by microbial antigens. Although thymus is the major source of the peripheral Tregs, peripheral conversion from Tconvs to Tregs have also been shown to occur under various experimental conditions. It remains less clear about the frequency of lineage conversion from Tconvs to Tregs in naïve animals. Here we used a newly established reporter system to track a group of post expansion Tregs (eTregs), which exhibited a stronger suppressive ability than the non-lineage marked Tregs. Notably, microbial antigens are the primary driver for the formation of eTregs. TCR repertoire analysis of Peyer's patch T cells revealed that eTregs are clonally related to Tconvs, but not to the non-lineage tracked Tregs. Adoptive transfer of Tconvs into lymphopenic hosts demonstrated a conversion from Tconvs to eTregs. Thus, our lineage tracking method was able to capture the lineage conversion from microbial activated effector T cells to Tregs in naïve animals. This study suggests that a fraction of clonally activated T cells from the natural T cell repertoire exhibits lineage conversion to Tregs in response to commensal microbes under homeostatic conditions.